How to apply hyperparameter optimization on Monte Carlo Tree Search?
Since no one answers, I will say the approach I used.
It was inspired by the successive halving algorithm. The concept is that we find good performing hyperparameters for one agent by playing manually against it. Then, we put the agent we want to tune to play against it. We assign different configurations. All of these configurations are tried for a predefined number of games per configuration. We keep the best performing half, and then go to the next round with the half we kept and repeat this process. The last configuration that remains is the best performing configuration.